GROWTH MINDSET MODERATES THE IMPACT OF NEONATAL RESUSCITATION SKILL MAINTENANCE ON PERFORMANCE IN A SIMULATION TRAINING VIDEO GAME
Bibliographic record
Abstract
Abstract BACKGROUND The Joint Commission on Accreditation of Healthcare Organizations (2004) reporting on preventing infant death and injury during delivery identified human errors during neonatal resuscitation as responsible for more than two thirds of perinatal mortality and morbidity. One of the main causes of human error in neonatal resuscitation stems from a lack of practical learning experiences highlighted by the neonatal training paradox of high-acuity, low-occurrence (HALO) situations that arise infrequently. simulation-based medical education (SBME) is resource and cost intensive, and not offered frequently enough for development of competency and for supporting knowledge retention. Therefore, other methods of training to improve knowledge retention and decision-making are needed. We therefore developed a complementary tool to the physical SBME to improve knowledge retention during neonatal resuscitation in the delivery room. Specifically, we developed a game-based neonatal resuscitation training simulator called RETAIN. OBJECTIVES We hypothesized that HCP playing the video game will have an improved mindset and therefore an improved neonatal resuscitation performance. DESIGN/METHODS HCPs trained in NRP, including registered nurses, respiratory therapists, neonatal nurse practitioners, neonatal consultants, and neonatal fellows were recruited from the Royal Alexandra Hospital, a tertiary NICU. Each participant was asked to complete a pre-game questionnaire to obtain demographics (e.g. last Neonatal Resuscitation Course (NRP)-course, years of experience) and assess their neonatal resuscitation knowledge by completing a Resuscitation scenario. Afterwards each participant played the RETAIN simulator, which started with a tutorial before the actual three rounds and there was a countdown for each of the rounds to simulate the stress of a real-world scenario. After completion of the game each participant also completed a Post-game questionnaire to assess the player’s mindset (e.g. How much do you agree with the following statements? You can always change how good you are at your job or You can get better at your job with practice) using a Likert scale (1=Strongly Disagree to 5=Strongly agree). RESULTS We recruited 50 (45 females, 4 males, and 1 not reported) HCP who were all NRP-trained and had completed a NRP refresher course within the last 24 months. Participants needed a mean (SD) 8.47 (8.66) minutes to complete the game. On average, participants reported high levels of growth mindset (with scores ranging from seven to ten), took their latest NRP course more than eight months prior to the current study, and scored 93% in the game (32 was a perfect score). Interestingly, participants who took the NRP course more recently made more mistakes in the simulation game. There was a significant interaction of Last NRP Course and Growth Mindset in predicting Number of Tries (b =.09, S.E.=.04, beta=.32, t=2.25, p=.03), as well as a main effect for Last NRP Course (b= -.08, S.E.=.04, beta=-.30, t=-2.04, p<.05). Thus, participants who took an NRP course recently (i.e., within eight months), before the current study, completed the game in significantly fewer tries when they endorsed more rather than less of a growth mindset. However, participants who endorsed more of a growth mindset performed similarly on the game regardless of when they took the NRP course. CONCLUSION The study examined the relation between HCP task performance and time elapsed since their latest NRP course and found that growth mindset moderates this relation. Specifically, HCP who took the NRP course within the past eight months, those who endorsed a higher growth mindset made fewer mistakes in a simulation game. Some implications include growth mindset interventions and increased opportunities to practice skills in simulation sessions to help HCP achieve better performance after taking a refresher NRP course.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".